Single and binary ion sorption equilibria of monovalent and divalent ions in commercial ion exchange membranes
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The superconducting gap defines the fundamental energy scale for the emergence of dissipationless transport and collective phenomena in a superconductor. In layered high-temperature cuprate superconductors, in which the Cooper pairs are confined to weakly coupled two-dimensional (2D) copper–oxygen (CuO 2 ) planes, terahertz (THz) spectroscopy at subgap millielectronvolt (meV) energies has provided crucial insights into the collective superfluid response perpendicular to the superconducting layers. However, within the CuO 2 planes, the collective superfluid response manifests as plasmonic charge oscillations at energies far exceeding the superconducting gap, obscured by strong dissipation. Here, in this study, we present spectroscopic evidence of a below-gap, 2D superfluid plasmon in few-layer Bi 2 Sr 2 CaCu 2 O 8+x and spatially resolve its deeply subdiffractive THz electrodynamics. By placing the superconductor in the near field of a spintronic THz emitter, we reveal this distinct resonance—absent in bulk samples and observed only in the superconducting phase—and determine its plasmonic nature by mapping the geometric anisotropy and dispersion. Crucially, these measurements offer a direct view of the momentum-dependent and frequency-dependent superconducting transition in two dimensions.
Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial neurons and synapses are, however, currently limited by the energy and area requirements of these components. Spintronic nanodevices, which exploit both the magnetic and electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits, and magnetic tunnel junctions are of particular interest as neuromorphic computing elements because they are compatible with standard integrated circuits and can support multiple functionalities. Here, we review the development of spintronic devices for neuromorphic computing. We examine how magnetic tunnel junctions can serve as synapses and neurons, and how magnetic textures, such as domain walls and skyrmions, can function as neurons. We also explore spintronics-based implementations of neuromorphic computing tasks, such as pattern recognition in an associative memory, and discuss the challenges that exist in scaling up these systems.
An important goal of spintronics is to covert a charge current into a spin current with a controlled spin polarization that can exert torques on an adjacent magnetic layer. Here we demonstrate such torques in a two ferromagnet system. A CoNi multilayer is used as a spin current source in a sample with structure CoNi/Au/CoFeB. Spin torque ferromagnetic resonance is used to measure the torque on the CoFeB layer. The response as a function of the applied field angle and current is consistent with the symmetry expected for a torque produced by the planar Hall effect originating in CoNi. We find the strength of this effect to be comparable to that of the spin Hall effect in platinum, indicating that the planar Hall effect holds potential as a spin current source with a controllable polarization direction.
Alongside the rising global water demand, continued stress on current water supplies has sparked interest in using nontraditional source waters for energy, agriculture, industry, and domestic needs. Membrane technologies have emerged as one of the most promising approaches to achieve water security, but implementation of membrane processes for increasingly complex waters remains a challenge. The technical feasibility of membrane processes replacing conventional treatment of alternative water supplies (e.g., wastewater, seawater, and produced water) is considered in the context of typical and emerging water quality goals. This review considers the effectiveness of current technologies (both conventional and membrane based), as well as the potential for recent advancements in membrane research to achieve these water quality goals. We envision the future of water treatment to integrate advanced membranes (e.g., mixed-matrix membranes, block copolymers) into smart treatment trains that achieve several goals, including fit-for-purpose water generation, resource recovery, and energy conservation.
The new monthly Dynamic Mesoscale Material Science Capability Seminar Series features Lab highlights from materials science areas, including cutting-edge research at national and international light sources, in-depth investigations of technical gaps, and advanced studies of accelerator technologies that might provide probes in this region.
NNSA has an unmet capability gap to predict how changes in material microstructure, due to aging and new manufacturing practices, can affect performance at extremes. This capability gap is the basis for the Dynamic Mesoscale Material Science Capability (DMMSC) CD-0 that was signed in 2015 [1]. Because of the delay to the MaRIE facility approach, the Tri-Lab in March 2020 proposed separate complementary investments at the APS and LCLS to address portions of the DMMSC mission need [2].
Flat bands in graphene-based moiré structures host a wide range of emerging strongly correlated and topological phenomena. Optically probing and controlling them can reveal important information such as symmetry and dynamics, but have so far been challenging due to the small energy gap compared to optical wavelengths. Here, we report near infrared optical control of orbital magnetism and associated anomalous Hall effects (AHE) in a magic angle twisted bilayer graphene (MATBG) on monolayer WSe$_2$ device. We show that the properties of the AHE, such as hysteresis and amplitude, can be controlled by light near integer moiré fillings, where spontaneous ferromagnetism exists. By modulating the light helicity, we observe periodic modulation of the transverse resistance in a wide range of fillings, indicating light induced orbital magnetization through a large inverse Faraday effect. At the transition between metallic and AHE regimes, we also reveal large and random switching of the Hall resistivity, which are attributed to optical control of percolating cluster of magnetic domains. Our results open the door to optical manipulation of correlation and topology in MATBG and related structures.
Exotic electronic physics including correlated insulating states and fractional Chern insulators have been observed in twisted bilayer graphene in a magnetic field when the Fermi velocity vanishes, however a question remains as to the stability of these states which is controlled by the gap to the first excited state. Free-space terahertz magneto-optics can directly probe the gap to charge excitations which bounds the stability of electronic states, but this measurement has thus-far been inaccessible due to the micron size of twisted bilayer graphene samples, while the wavelength of terahertz light is up to a millimeter. Here we leverage advances in fabrication to create twisted bilayer graphene samples over 5 mm x 5 mm in size with a uniform twist angle and study the magnetic field dependence of the cyclotron resonance by a complex Faraday rotation experiment in p-doped large angle twisted bilayer graphene. These measurements directly probe charge excitations in inter-Landau level transitions and determine the Fermi velocity as a function of twist angle.
The discovery of topological phases has introduced a new dimension to materials science. Three-dimensional (3D) topological insulators (TIs) are a remarkable class of matter that is insulating in the bulk while hosting conductive topological surface states (TSSs) with unique charge and spin properties. High-order harmonic generation (HHG) has emerged as a powerful tool to probe condensed matter systems by providing insights into their electronic structure and dynamic behavior. Here, we investigate HHG in the prototype 3D-TI Bi$_2$Se$_3$. We demonstrate that the contributions of bulk and surface states to the harmonic emission can be controlled by tuning the thickness of thin film samples. An ultrathin (6 nm) film substantially enhances HHG from the surface states, while the bulk states dominate HHG in a thicker (50 nm) film. By applying a quasi-static terahertz perturbing field, we disentangle the bulk and surface responses and reveal the significant impact of the surface states' shift vector and Berry curvature on HHG. Our study provides effective methods for isolating the optical responses of TSSs from those of the bulk, which opens the door to resolving an ongoing debate regarding whether it is possible to reliably extract topological signatures in HHG.
The role that dynamics plays in estimating the state of the atmosphere from incomplete and noisy data is reviewed. Objective analysis represents an attempt at relying mostly on the data and minimizing the role of dynamics in the estimation. Data assimilation tries to balance properly the roles of dynamical and observational information. Sequential estimation is presented as the proper framework for understanding this balance, and the Kalman filter as the ideal, optimal procedure for data assimilation. The optimal filter computes forecast error covariances of a given atmospheric model exactly, and hence data assimilation should be closely connected with predictability studies. This connection is described, and consequences drawn for currently active areas of the atmospheric and related sciences, namely, mesoscale meteorology, long range forecasting, and upper ocean dynamics. Possibilities offered by judicious data assimilation in understanding barotropic adjustment, a phenomenon that appears to play a crucial role in atmospheric behavior on the scale of weeks to months, and hence in long range forecasting are addressed.
The central theme of this review article is the role that dynamics plays in estimating the state of the atmosphere and of the ocean from incomplete and noisy data. Objective analysis and inverse methods represent an attempt at relying mostly on the data and minimizing the role of dynamics in the estimation. Four-dimensional data assimilation tries to balance properly the roles of dynamical and observational information. Sequential estimation is presented as the proper framework for understanding this balance, and the Kalman filter as the ideal, optimal procedure for data assimilation. The optimal filter computes forecast error covariances of a given atmospheric or oceanic model exactly, and hence data assimilation should be closely connected with predictability studies. This connection is described, and consequences drawn for currently active areas of the atmospheric and oceanic sciences, namely, mesoscale meteorology, medium and long-range forecasting, and upper-ocean dynamics.